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» Incremental and Decremental Support Vector Machine Learning
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BMCBI
2008
170views more  BMCBI 2008»
13 years 8 months ago
A genetic approach for building different alphabets for peptide and protein classification
Background: In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task i...
Loris Nanni, Alessandra Lumini
BMCBI
2008
118views more  BMCBI 2008»
13 years 8 months ago
Virtual screening of GPCRs: An in silico chemogenomics approach
The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules is ...
Laurent Jacob, Brice Hoffmann, Véronique St...
BMCBI
2007
173views more  BMCBI 2007»
13 years 8 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
BMCBI
2007
93views more  BMCBI 2007»
13 years 8 months ago
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition
Background: Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing ne...
Iain Melvin, Eugene Ie, Rui Kuang, Jason Weston, W...
ICASSP
2011
IEEE
13 years 8 days ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...